diff --git a/00.value-of-dataviz/slides.md b/00.value-of-dataviz/slides.md index 1394bbd..4bac72d 100644 --- a/00.value-of-dataviz/slides.md +++ b/00.value-of-dataviz/slides.md @@ -335,7 +335,7 @@ Use of Altair is **strongly** recommended, but other libraries allowed. --- -## Acknowledgements +## Acknowledgements & References Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials. diff --git a/01.gog-altair/slides.md b/01.gog-altair/slides.md index a255a80..3559123 100644 --- a/01.gog-altair/slides.md +++ b/01.gog-altair/slides.md @@ -6,9 +6,9 @@ ## Today -- Grammar of Graphics -- Types of Data -- Intro to Altair +- What is a **grammar of graphics** and how do we use it in practice? +- What **types of data** do we encounter, and how does that affect visualizations? +- Introduction to **Altair** --- diff --git a/02.perception-and-color/3d-scatter.png b/02.perception-and-color/3d-scatter.png new file mode 100644 index 0000000..4d3ecea Binary files /dev/null and b/02.perception-and-color/3d-scatter.png differ diff --git a/02.perception-and-color/datavizproject.png b/02.perception-and-color/datavizproject.png new file mode 100644 index 0000000..f8d5d80 Binary files /dev/null and b/02.perception-and-color/datavizproject.png differ diff --git a/02.perception-and-color/effectiveness.png b/02.perception-and-color/effectiveness.png new file mode 100644 index 0000000..8df91b6 Binary files /dev/null and b/02.perception-and-color/effectiveness.png differ diff --git a/02.perception-and-color/example1a.png b/02.perception-and-color/example1a.png new file mode 100644 index 0000000..2d7b811 Binary files /dev/null and b/02.perception-and-color/example1a.png differ diff --git a/02.perception-and-color/example1b.png b/02.perception-and-color/example1b.png new file mode 100644 index 0000000..ab3a53a Binary files /dev/null and b/02.perception-and-color/example1b.png differ diff --git a/02.perception-and-color/perception-examples.ipynb b/02.perception-and-color/perception-examples.ipynb new file mode 100644 index 0000000..1954899 --- /dev/null +++ b/02.perception-and-color/perception-examples.ipynb @@ -0,0 +1,541 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "adf7aa23-daf2-4cfe-9910-2b00576b2ff7", + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "import altair as alt" + ] + }, + { + "cell_type": "markdown", + "id": "0726aa55-4e9f-4f0f-a522-dc231c3f2c41", + "metadata": {}, + "source": [ + "These are examples used in class, there's no additional content here, but you can see how the examples were created." + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "05a4d76e-7ade-431a-a773-d438c6ac9c79", + "metadata": {}, + "outputs": [], + "source": [ + "random_df = pl.DataFrame({\n", + " \"a\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],\n", + " \"b\": [\"a\", \"b\", \"q\", \"c\", \"a\", \"c\", \"n\", \"c\", \"p\", \"b\", \"c\", \"n\", \"q\", \"r\", \"a\", \"b\", \"c\", \"b\", \"b\", \"b\"],\n", + " \"c\": [500, 2, 3, 4, 10, 20, 100, 490, 400, 140, 200, 180, 380, 350, 180, 135, 400, 210, 230, 300]\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "902298fe-3a6c-446e-a060-f95071acedf1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(random_df).mark_point().encode(\n", + " alt.X(\"a\"),\n", + " alt.Y(\"c\"),\n", + " alt.Color(\"b\", legend=None),\n", + " alt.Size(\"c\", legend=None),\n", + " alt.Shape(\"a:N\", legend=None),\n", + " alt.Fill(\"b\", legend=None),\n", + " alt.Opacity(\"b\", legend=None),\n", + ") " + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "87228f27-baad-4acb-80af-1ca44c21e8fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "5515ea60-70c1-4fd7-bef9-b5451c2ea00c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(random_df).mark_line().encode(\n", + " x=\"a\",\n", + " y=\"c\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "ba3df4b2-e64e-4574-b104-068ebe8d76c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stevens = {\"brightness\": 0.4, \"depth perception\": 0.63, \"area\": 0.7, \"2D position\": 1, \n", + " \"color saturation\": 1.7, \n", + " # \"electric shock\": 3.5\n", + " }\n", + "\n", + "rows = []\n", + "for x in [0,0.2,0.4,0.6, 0.8, 1.0, 1.25, 1.5, 1.75, 2, 3]:\n", + " for cat, exponent in stevens.items():\n", + " rows.append({\"category\": cat, \"delta-stimulus\": x, \"response\": x**exponent})\n", + "\n", + "stevens_df = pl.DataFrame(rows)\n", + "\n", + "alt.Chart(stevens_df).mark_line().encode(\n", + " alt.Color(\"category\", legend=None),\n", + " alt.X(\"delta-stimulus\"),\n", + " alt.Y(\"response\"),\n", + ") + alt.Chart(stevens_df).mark_text(align=\"left\", dx=2).encode(\n", + " alt.Color(\"category\"),\n", + " alt.Text(\"category\"),\n", + " alt.X(\"delta-stimulus\", aggregate=\"max\"),\n", + " alt.Y(\"response\", aggregate=\"max\"),\n", + ").properties()" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "95dc101c-1dda-413a-87fb-b700fe5f43e3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ad736e1-0e19-46d1-8e93-2b1409ffffc1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/02.perception-and-color/slides.html b/02.perception-and-color/slides.html new file mode 100644 index 0000000..347e009 --- /dev/null +++ b/02.perception-and-color/slides.html @@ -0,0 +1,220 @@ +
+

Perception & Color

+

CAPP 30239

+
+
+

Today

+
    +
  • What matters most when creating a visualization?
    • +
  • How does human perception factor into visualization design?
    • +
  • Understanding color, and computational representations of it.
    • +
+
+
+

What is the most important question when creating a visualization?

+
+
+

What is the most important question when creating a visualization?

+
    +
  • Where will the data come from? +
  • What type of chart do I use?
    • +
  • Who is the audience?
    • +
+
+
+

Audience First

+
    +
  • Who are you presenting to?
    • +
  • How familiar are they with the data?
    • +
  • What is their numerical & visualization literacy?
    • +
  • Via what medium will they receive the information?
    • +
  • What are you trying to do? (Persuade, Inform, Inspire?)
    • +
+

Only now can we start thinking about data and presentation.

+
+
+

Perception

+
    +
  • Selective - We can only pay attention to so much.
    • +
  • Patterns - Our brains are pattern-matching machines, audience will benefit from intentional patterns & be distracted by unintentional ones.
    • +
  • Limited working memory - We hold a very limited set of information in our minds at once.
    • +
+
+
+

What do you see?

+
+
+

+
+
alt.Chart(random_df).mark_point().encode(
+    alt.X("a"),
+    alt.Y("c"),
+    alt.Color("b"),
+    alt.Size("c"),
+    alt.Shape("a:N"),
+    alt.Fill("b"),
+    alt.Opacity("b"),
+) 
+
+
+
+
+
+

What do you see?

+
+
+

+
alt.Chart(random_df).mark_line().encode(
+    x="a",
+    y="c",
+)`
+
+
+
+
+
+

Effectiveness Revisited

+

+
+
+
+
+

Altair Channels

+
    +
  • Position (X, Y)
    • +
  • Angle (Angle)
    • +
  • Area (Radius, Size)
    • +
  • Hue, Saturation (Color)
    • +
  • Texture (Opacity, Fill)
    • +
  • Shape (mark type, Shape)
    • +
+
+
+

What about?

+
    +
  • Length
    • +
  • Slope
    • +
  • Volume
    • +
  • Density
    • +
  • Connection
    • +
  • Containment
    • +
+
+
+
+
+

Derived Properties

+
    +
  • Length/Area - size of bars (X, Y)
    • +
  • Slope & Density - affected by scale
    • +
  • Connection - ex. layering of lines w/ points
    • +
  • Containment - achieved with layering
    • +
+

What about volume?

+
+
+

Stevens' Power Law

+

Stevens (1975): Human response to sensory stimulus is characterized by a power law with different exponents with different stimuli.

+

perception = (magnitude of sensation)a

+

Smaller a exponent: harder to perceive changes.

+

Stevens measured values of a by exposing people to varied stimulus and asking them to compare magnitudes.

+
+
+
+

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ContinuumExponent
Color Brightness0.33-0.5
Smell0.6
Loudness0.67
Depth Perception0.67
Area0.7
2D Planar Position1.0
Warmth1.3-1.6
Color Saturation1.7
Electric Shock3.5
+
+
+
+

3D Graphs

+

+
+
+

+
+
+

+
+
+

Instead of 3D Graphs

+
    +
  • Find other channels: hue & size are good candidates.
    • +
  • Combine different dimensions into side-by-side 2D graphs.
    • +
+

TODO: example of 2D decomposition of a graph

+
+
+

Color

+
+
+

Acknowledgements & References

+

Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials.

+ +
+
\ No newline at end of file diff --git a/02.perception-and-color/slides.md b/02.perception-and-color/slides.md new file mode 100644 index 0000000..bd2d4ed --- /dev/null +++ b/02.perception-and-color/slides.md @@ -0,0 +1,211 @@ +--- +theme: custom-theme +--- + +# Perception & Color + +## CAPP 30239 + +--- + +## Today + +- What matters most when creating a visualization? +- How does human **perception** factor into visualization design? +- Understanding **color**, and computational representations of it. + +--- + +## What is the most important question when creating a visualization? + +--- + +## What is the most important question when creating a visualization? + + + +--- + +## Audience First + +- Who are you presenting to? +- How familiar are they with the data? +- What is their numerical & visualization literacy? +- Via what medium will they receive the information? +- What are you trying to do? (Persuade, Inform, Inspire?) + +*Only now can we start thinking about data and presentation.* + +--- + +## Perception + +- **Selective** - We can only pay attention to so much. +- **Patterns** - Our brains are pattern-matching machines, audience will benefit from intentional patterns & be distracted by unintentional ones. +- **Limited working memory** - We hold a very limited set of information in our minds at once. + +--- + +## What do you see? + +
+
+ +![](example1a.png) + +
+ +```python +alt.Chart(random_df).mark_point().encode( + alt.X("a"), + alt.Y("c"), + alt.Color("b"), + alt.Size("c"), + alt.Shape("a:N"), + alt.Fill("b"), + alt.Opacity("b"), +) +``` +
+
+ +--- + +## What do you see? + +
+
+ +![](example1b.png) + +``` +alt.Chart(random_df).mark_line().encode( + x="a", + y="c", +)` +``` + +
+
+ +--- + +## Effectiveness Revisited + +![width:800px](effectiveness.png) + +--- + +
+ +
+ +**Altair Channels** + +- Position (`X, Y`) +- Angle (`Angle`) +- Area (`Radius`, `Size`) +- Hue, Saturation (`Color`) +- Texture (`Opacity`, `Fill`) +- Shape (mark type, `Shape`) +
+ +
+ +**What about?** +- Length +- Slope +- Volume +- Density +- Connection +- Containment +
+
+ +--- + +**Derived Properties** + + +- Length/Area - size of bars (`X`, `Y`) +- Slope & Density - affected by scale +- Connection - ex. layering of lines w/ points +- Containment - achieved with layering + +What about *volume*? + +--- + +## Stevens' Power Law + +Stevens (1975): Human response to sensory stimulus is characterized by a power law with different exponents with different stimuli. + +perception = (magnitude of sensation)a + +Smaller a exponent: harder to perceive changes. + +Stevens measured values of a by exposing people to varied stimulus and asking them to compare magnitudes. + +--- + + +
+ +![](stevens.png) + +
+ + +| Continuum | Exponent | +|-|-| +| Color Brightness| 0.33-0.5 | +| Smell| 0.6 | +| Loudness | 0.67 | +| **Depth Perception** | 0.67 | +| Area | 0.7 | +| 2D Planar Position | 1.0 | +| Warmth | 1.3-1.6 | +| Color Saturation | 1.7 | +| Electric Shock | 3.5 | + +
+ +--- + +## 3D Graphs + +![](stunning-3d-chart.jpg) + +--- + +![](datavizproject.png) + +--- + +![](3d-scatter.png) + +--- + +## Instead of 3D Graphs + +- Find other channels: hue & size are good candidates. +- Combine different dimensions into side-by-side 2D graphs. + +TODO: example of 2D decomposition of a graph + +--- + +## Color + +--- + +## Acknowledgements & References + +Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials. + +- https://www.math.csi.cuny.edu/~mvj/GC-DataViz-S23/lectures/L6.html +- https://en.wikipedia.org/wiki/Stevens%27s_power_law diff --git a/02.perception-and-color/stevens.png b/02.perception-and-color/stevens.png new file mode 100644 index 0000000..a184a6f Binary files /dev/null and b/02.perception-and-color/stevens.png differ diff --git a/02.perception-and-color/stunning-3d-chart.jpg b/02.perception-and-color/stunning-3d-chart.jpg new file mode 100644 index 0000000..a7d8771 Binary files /dev/null and b/02.perception-and-color/stunning-3d-chart.jpg differ diff --git a/custom-theme.css b/custom-theme.css new file mode 100644 index 0000000..be56af3 --- /dev/null +++ b/custom-theme.css @@ -0,0 +1,11 @@ +/* custom-theme.css */ +/* @theme custom-theme */ + +@import "default"; + +.container { + display: flex; +} +.col { + flex: 1; +}